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@@ -95,9 +95,9 @@ model = AutoModel(model="paraformer-zh", model_revision="v2.0.2", \
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vad_model="fsmn-vad", vad_model_revision="v2.0.2", \
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punc_model="ct-punc-c", punc_model_revision="v2.0.2", \
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spk_model="cam++", spk_model_revision="v2.0.2")
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-res = model(input=f"{model.model_path}/example/asr_example.wav",
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- batch_size=64,
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- hotword='魔搭')
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+res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
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+ batch_size=64,
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+ hotword='魔搭')
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print(res)
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```
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Note: `model_hub`: represents the model repository, `ms` stands for selecting ModelScope download, `hf` stands for selecting Huggingface download.
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@@ -124,7 +124,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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- res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
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+ res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size, encoder_chunk_look_back=encoder_chunk_look_back, decoder_chunk_look_back=decoder_chunk_look_back)
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print(res)
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```
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Note: `chunk_size` is the configuration for streaming latency.` [0,10,5]` indicates that the real-time display granularity is `10*60=600ms`, and the lookahead information is `5*60=300ms`. Each inference input is `600ms` (sample points are `16000*0.6=960`), and the output is the corresponding text. For the last speech segment input, `is_final=True` needs to be set to force the output of the last word.
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@@ -135,7 +135,7 @@ from funasr import AutoModel
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model = AutoModel(model="fsmn-vad", model_revision="v2.0.2")
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wav_file = f"{model.model_path}/example/asr_example.wav"
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-res = model(input=wav_file)
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+res = model.generate(input=wav_file)
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print(res)
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```
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### Voice Activity Detection (Non-streaming)
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@@ -156,7 +156,7 @@ total_chunk_num = int(len((speech)-1)/chunk_stride+1)
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for i in range(total_chunk_num):
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speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride]
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is_final = i == total_chunk_num - 1
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- res = model(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
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+ res = model.generate(input=speech_chunk, cache=cache, is_final=is_final, chunk_size=chunk_size)
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if len(res[0]["value"]):
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print(res)
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```
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@@ -165,7 +165,7 @@ for i in range(total_chunk_num):
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from funasr import AutoModel
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model = AutoModel(model="ct-punc", model_revision="v2.0.2")
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-res = model(input="那今天的会就到这里吧 happy new year 明年见")
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+res = model.generate(input="那今天的会就到这里吧 happy new year 明年见")
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print(res)
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```
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### Timestamp Prediction
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@@ -175,7 +175,7 @@ from funasr import AutoModel
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model = AutoModel(model="fa-zh", model_revision="v2.0.2")
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wav_file = f"{model.model_path}/example/asr_example.wav"
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text_file = f"{model.model_path}/example/text.txt"
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-res = model(input=(wav_file, text_file), data_type=("sound", "text"))
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+res = model.generate(input=(wav_file, text_file), data_type=("sound", "text"))
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print(res)
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```
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[//]: # (FunASR supports inference and fine-tuning of models trained on industrial datasets of tens of thousands of hours. For more details, please refer to ([modelscope_egs](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_pipeline/quick_start.html)). It also supports training and fine-tuning of models on academic standard datasets. For more details, please refer to([egs](https://alibaba-damo-academy.github.io/FunASR/en/academic_recipe/asr_recipe.html)). The models include speech recognition (ASR), speech activity detection (VAD), punctuation recovery, language model, speaker verification, speaker separation, and multi-party conversation speech recognition. For a detailed list of models, please refer to the [Model Zoo](https://github.com/alibaba-damo-academy/FunASR/blob/main/docs/model_zoo/modelscope_models.md):)
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@@ -229,10 +229,16 @@ The use of pretraining model is subject to [model license](./MODEL_LICENSE)
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}
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@inproceedings{gao22b_interspeech,
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author={Zhifu Gao and ShiLiang Zhang and Ian McLoughlin and Zhijie Yan},
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- title={{Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition}},
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+ title={Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition},
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year=2022,
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booktitle={Proc. Interspeech 2022},
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pages={2063--2067},
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doi={10.21437/Interspeech.2022-9996}
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}
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+@inproceedings{shi2023seaco,
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+ author={Xian Shi and Yexin Yang and Zerui Li and Yanni Chen and Zhifu Gao and Shiliang Zhang},
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+ title={SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability},
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+ year={2023},
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+ booktitle={ICASSP2024}
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+}
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```
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shixian.shi